Computing Information Quantity as Similarity Measure for Music Classification Task

TL;DR

This paper introduces a new, scalable, and clear information quantity-based similarity measure for music classification, outperforming compression-based methods in composer estimation accuracy.

Contribution

The paper presents a novel similarity measure based on information quantity that replaces compression-based dissimilarity measures, offering improved clarity and scalability.

Findings

The proposed method outperforms CDM in composer estimation accuracy.

It has lower computational complexity than CDM.

It is easier to reproduce results due to formalization.

Abstract

This paper proposes a novel method that can replace compression-based dissimilarity measure (CDM) in composer estimation task. The main features of the proposed method are clarity and scalability. First, since the proposed method is formalized by the information quantity, reproduction of the result is easier compared with the CDM method, where the result depends on a particular compression program. Second, the proposed method has a lower computational complexity in terms of the number of learning data compared with the CDM method. The number of correct results was compared with that of the CDM for the composer estimation task of five composers of 75 piano musical scores. The proposed method performed better than the CDM method that uses the file size compressed by a particular program.